PGC-1α-induced improvements in skeletal muscle metabolism and insulin sensitivityThis paper is one of a selection of papers published in this Special Issue, entitled 14th International Biochemistry of Exercise Conference – Muscles as Molecular and Metabolic Machines, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
The peroxisome proliferator-activated receptor gamma (PPARgamma) coactivator 1alpha (PGC-1alpha), a nuclear encoded transcriptional coactivator, increases the expression of many genes in skeletal muscle, including those involved with fatty acid oxidation and oxidative phosphorylation. Exercise increases the expression of PGC-1alpha, and the exercise-induced upregulation of many genes is attributable, in part, to the preceding activation and upregulation of PGC-1alpha. Indeed, PGC-1alpha overexpression, like exercise training, increases exercise performance. PGC-1alpha reductions in humans have been observed in type 2 diabetes, while, in cell lines, PGC-1alpha mimics the exercise-induced improvement in insulin sensitivity. However, unexpectedly, in mammalian muscle, PGC-1alpha overexpression contributed to the development of diet-induced insulin resistance. This may have been related to the massive overexpression of PGC-1alpha, which induced the upregulation of the fatty acid transporter FAT/CD36 and led to an increase in intramuscular lipids, which interfere with insulin signalling. In contrast, when PGC-1alpha was overexpressed modestly, within physiological limits, mitochondrial fatty acid oxidation was increased, GLUT4 expression was upregulated, and insulin-stimulated glucose transport was increased. More recently, similar PGC-1alpha-induced improvements in the insulin-resistant skeletal muscle of obese Zucker rats have been observed. These studies suggest that massive PGC-1alpha overexpression, but not physiologic PGC-1alpha overexpression, induces deleterious metabolic effects, and that exercise-induced improvements in insulin sensitivity are induced, in part, by the exercise-induced upregulation of PGC-1alpha.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".